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Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events
In the third and fourth studies, end-to-end networks are developed and tested as a new solution to directly detect cracks and spalling in the image collections of recent large earthquakes.
Automatic Displacement and Vibration Measurement in Laboratory Experiments with A Deep Learning Method
This paper proposes a pipeline to automatically track and measure displacement and vibration of structural specimens during laboratory experiments.
End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales
Robust Mask R-CNN (Mask Regional Convolu-tional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earth-quakes.
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